{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Snippets and Programs from Chapter 2: Visualizing Data with Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DICAIguwjHSBrXVJVhGFIGIZ9v06WdfIWsBX4b6IfrHYyBrxFtDyzCniEaAknqd1ut3sb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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a986ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1af89c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f0YaAFTNbzlVjGeUextPkltH+fHp9ljOP9BfmOdKW14nc/lLXZ9knVOtQ6GEnFXKUtPc5\nSnoH0z5g+YxWNPvV8jCexk12Pr0+y7lC2upaCBwkbWvFuTGFr8/pnrm/Qfofe8ySbJuqeYcP/ir3\nAunDZjfPXDmz3jXA88A3SIMmz+uznG7n0+uzmovAAeC3c9tLXZ/THe77gc3Z8iBwgdy/P6NS+vng\nN/mqbDnfp1OqAfwj6V912z1lDV6fZRQ5n16fxd1C2kMHuB64h/QOo1Yzen3+C/DfwP+R9oa+iA87\n9aLb+fwy6W1ox4B/J/0PrvZ8GK9eRc6n12dxv0naxjoGvAo8kG33+pQkSZIkSZIkSZIkSZIkSZIk\nSZI0O/0/bZWo72EMB9gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a931be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P32-35: Simple plot example\n",
    ">>> x_numbers = [1, 2, 3]\n",
    ">>> y_numbers = [2, 4, 6]\n",
    ">>> from pylab import plot, show\n",
    ">>> plot(x_numbers, y_numbers)\n",
    ">>> show()\n",
    ">>> plot(x_numbers, y_numbers, marker='o')\n",
    ">>> show()\n",
    ">>> plot(x_numbers, y_numbers, 'o')\n",
    ">>> show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vpQ1vQYHTkSizFBTI1bR1ddY9h5ZwrOeJZJ9O9foIryb7V16R6XOnnup0JMos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\nlFJKKaWUUkoppZRSSimllFJKKeU2/w/exvcYtiB2ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a98a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P35: Plot the temperature in New York City\n",
    ">>> nyc_temp = [53.9, 56.3, 56.4, 53.4, 54.5, 55.8, 56.8, 55.0, 55.3, 54.0,\n",
    "56.7, 56.4, 57.3]\n",
    ">>> plot(nyc_temp, marker='o')\n",
    ">>> show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XcHsmUBp2/PeAjcin/btQSimllFJKJa8+wLvAOuTpoSC4vRtQDnwC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\nKaWUUkoppZRSyir/B8qKLCK7z0piAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a9c1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P37: Plot the temperature in New York City with the years on the X-axis\n",
    ">>> nyc_temp = [53.9, 56.3, 56.4, 53.4, 54.5, 55.8, 56.8, 55.0, 55.3, 54.0,\n",
    "56.7, 56.4, 57.3]\n",
    ">>> years = range(2000, 2013)\n",
    ">>> plot(years, nyc_temp, marker='o')\n",
    ">>> show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vVI/bfy0h7a516hcwHpFELgzi+cfnVFxYkfE1R9O20yQYPhxatw5xndTGRUw7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\nzrCLrL+yPi0Lt6RH2R4GDlY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a9600b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P39: Comparing the monthly temperature trends in New York City\n",
    ">>> nyc_temp_2000 = [31.3, 37.3, 47.2, 51.0, 63.5, 71.3, 72.3, 72.7, 66.0,\n",
    "57.0, 45.3, 31.1]\n",
    ">>> nyc_temp_2006 = [40.9, 35.7, 43.1, 55.7, 63.1, 71.0, 77.9, 75.8, 66.6,\n",
    "56.2, 51.9, 43.6]\n",
    ">>> nyc_temp_2012 = [37.3, 40.9, 50.9, 54.8, 65.1, 71.0, 78.8, 76.7, 68.8,\n",
    "58.0, 43.9, 41.5]\n",
    ">>> months = range(1, 13)\n",
    ">>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012)\n",
    ">>> show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9iXnz4K+/YN++0P6TlEyaVkTi4uEBEyboiT9mZjz0ekjLta1Is3sR04ZIEk9p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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a96a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P41: Adding a legend to the above graph\n",
    ">>> from pylab import legend\n",
    ">>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012)\n",
    ">>> legend([2000, 2006, 2012])\n",
    ">>> show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cI1qubyk+hSZ8x0tFdAIChHBwEGLo0IjdVeecniMarW4U/Rm/fClErlxC3LkT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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1aa18630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P42: Adding a title, legend and labels to a graph\n",
    ">>> from pylab import plot, show, title, xlabel, ylabel, legend\n",
    ">>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012)\n",
    ">>> title('Average monthly temperature in NYC')\n",
    ">>> xlabel('Month')\n",
    ">>> ylabel('Temperature')\n",
    ">>> legend([2000, 2006, 2012])\n",
    ">>> show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kkpQ4g1ySEmeQS1LiDHJJSpxBLkmJM8glKXEGuSQlziCXpMQZ5JKUOINckhJnkEtS4gxy\nSUqcQS5JiTPIJSlxBrkkJc4gl6TEGeSSlDiDXJISZ5BLUuIMcklKnEEuSYkzyCUpcQa5JCXOIJek\nxBnkkpQ4g1ySEmeQS1LiDHJJSpxBLkmJKyTIDwEeBJ4FngEuzs3fD7gPeAFYAowsRwElSTtXU8Ay\no3PDU8BewBPAmcBngD8D1wCXAfsCl3dZN4QQSlZYSdod1NTUQGH5DBRWI3+dGOIA/wc8DxwEnAEs\nzM1fSAx3SVI/K7aNvA44BngUGAW05ua35qYlSf1scBHL7gX8ErgEeLvLeyE3dNPc3Lx9PJPJkMlk\niiqgJFW7bDZLNpvt9fqFtsHsAbQAvwb+PTdvJZAhNr2MIXaITuiynm3kklSkcrSR1wD/CTxHe4gD\nLAam58anA4sK3agkqXQKSfxJwDJgBe3NJ1cAjwF3AGOBNcA5wPou61ojl6QiFVsjL3jBXjLIJalI\n5WhakSQNYAa5JCXOIJekxBnkkpQ4g1ySEmeQS1LiDHJJSpxBLkmJM8glKXEGuSQlziCXpMQZ5JKU\nOINckhJnkEtS4gxySUqcQS5JiTPIJSlxBrkkJc4gl6TEGeSSlDiDXJISZ5BLUuIMcklKnEEuSYkz\nyCUpcQa5JCXOIJekxBnkkpQ4g1ySEmeQS1LiCgnyHwKtwNMd5u0H3Ae8ACwBRpa+aJKkQhQS5DcD\n07rMu5wY5OOBB3LTkqQKqClwuTrgLuCo3PRKYAqxpj4ayAITelgvhBD6VkJJ2s3U1NRA4fnc6zby\nUcQQJ/c6qpd/R5LUR4NL8DdCbuhRc3Pz9vFMJkMmkynBJiWpemSzWbLZbK/X70vTSgZ4HRgDPIhN\nK5JUEv3VtLIYmJ4bnw4s6uXfkST1USGJ/xNix+b+xPbwrwF3AncAY4E1wDnA+h7WtUYuSUUqtkZe\n8IK9ZJBLUpH6q2lFkjRAGOSSlDiDXJISZ5BLUuIMcklKnEEuSYkzyCUpcQa5JCXOIJekxBnkkpQ4\ng1ySEmeQS1LiDHJJSpxBLkmJM8glKXEGuSQlziCXpMQZ5JKUOINckhJnkEtS4gxySUqcQS5JiTPI\nJSlxBrkkJc4gl6TEGeSSlDiDXJISZ5BLUuIMcklKXF+DfBqwEngRuKzvxZEkFasvQV4L/AcxzI8A\n/gn4QCkKlYpsNlvpIpRNNe8buH+pq/b9K1Zfgvx44CVgDbAF+CnwjyUoUzKq+cNUzfsG7l/qqn3/\nitWXID8IWNth+pXcPElSP+pLkIeSlUKS1Gs1fVj3BKCZ2EYOcAWwDfh2h2VeAur7sA1J2h2tBg7r\njw0Nzm2sDhgCPMVu1tkpSdXgH4BVxJr3FRUuiyRJkqS8an5Q6BDgQeBZ4Bng4soWp2xqgSeBuypd\nkDIYCfwCeB54jtjfUy2uIH42nwZ+DAytbHH67IdAK3F/8vYD7gNeAJYQj2eqetq/7xA/m8uB/wL2\nqUC5qCU2tdQBe1B9beejgaNz43sRm5aqaf/yvgTcDiyudEHKYCEwMzc+mAqdKGVQB/yB9vD+GTC9\nYqUpjb8HjqFz0F0DXJobvwz4Vn8XqoR62r9TaL+j8FtUaP/+Drinw/TluaFaLQI+VulClNjBwP3A\nyVRfjXwfYthVo/2IFYt9iV9QdwFTK1qi0qijc9CtBEblxkfnplNWR+f96+gTwG27+gPl+NGs3elB\noTrit+mjFS5HqV0LfJV4O2m1ORT4E3Az8D/AD4DhFS1R6bwJ/Bvwv8CrwHriF3K1GUVsjiD3Omon\ny6ZuJvCrXS1UjiDfXR4U2ovYznoJ8H8VLkspnQ68QWwf78tzBgPVYOBY4Mbc6ztUzxVjPfAvxArG\ngcTP6D9XskD9IFC9mTMH2Ezs69ipcgT5OmKHYN4hxFp5NdkD+CXxkmdRhctSaicCZwB/BH4CfBT4\nUUVLVFqv5IbHc9O/IAZ6Nfgw8FvgL8BWYkfZiRUtUXm0EptUAMYQKx7VZgZwKhX8Iq72B4VqiMF2\nbaUL0g+mUH1t5ADLgPG58WY6P42csgbinVR7Ej+nC4FZFS1RadTRvbMzfzfc5aTd2Qnd928a8c6j\n/StSmg6q+UGhScS246eIzQ9P0v4zBdVmCtV510oDsUZe0du7yuRS2m8/XEi8ekzZT4jt/ZuJfW+f\nIXbq3k913H7Ydf9mEm/bfpn2fLmxYqWTJEmSJEmSJEmSJEmSJEmSJEmSVHr/D/fpQt8WRpoZAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1af3a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P43: Customizing the axes limits\n",
    ">>> nyc_temp = [53.9, 56.3, 56.4, 53.4, 54.5, 55.8, 56.8, 55.0, 55.3, 54.0,\n",
    "56.7, 56.4, 57.3]\n",
    ">>> plot(nyc_temp, marker='o')\n",
    ">>> axis(ymin=0)\n",
    ">>> show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a99db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P43: Simple plot using pyplot\n",
    "'''\n",
    "Simple plot using pyplot\n",
    "'''\n",
    "import matplotlib.pyplot\n",
    "\n",
    "def create_graph():\n",
    "    x_numbers=[1,2,3]\n",
    "    y_numbers=[2,4,6]\n",
    "    matplotlib.pyplot.plot(x_numbers, y_numbers)\n",
    "    matplotlib.pyplot.show()\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    create_graph()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a9c1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P44: Importing matplotlib.pyplot as plt\n",
    "'''\n",
    "Simple plot using pyplot\n",
    "'''\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_graph():\n",
    "    x_numbers = [1, 2, 3]\n",
    "    y_numbers = [2, 4, 6]\n",
    "    plt.plot(x_numbers, y_numbers)\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    create_graph()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a9983c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P45: Saving the plots\n",
    ">>> from pylab import plot, savefig\n",
    ">>> x = [1, 2, 3]\n",
    ">>> y = [2, 4, 6]\n",
    ">>> plot(x, y)\n",
    ">>> savefig('mygraph.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3p9m1SzenEZHcoyTgs3Q3p3n99UpuuAE++STkgEREMlAS8Nn48SMpLLymxrLC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\nERERERERERFpLv4/3OKv53a/zesAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1a9f49e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P46: Relationship between gravitational force and distance\n",
    "'''\n",
    "The relationship between gravitational force and\n",
    "distance between two bodies\n",
    "'''\n",
    "import matplotlib.pyplot as plt\n",
    "# draw the graph\n",
    "def draw_graph(x, y):\n",
    "    plt.plot(x, y, marker='o')\n",
    "    plt.xlabel('Distance in meters')\n",
    "    plt.ylabel('Gravitational force in newtons')\n",
    "    plt.title('Gravitational force and distance')\n",
    "    plt.show()\n",
    "def generate_F_r():\n",
    "    # generate values for r\n",
    "    r = range(100, 1001, 50)\n",
    "    # empty list to store the calculated values of F\n",
    "    F = []\n",
    "    # constant, G\n",
    "    G = 6.674*(10**-11)\n",
    "    # two masses\n",
    "    m1 = 0.5\n",
    "    m2 = 1.5\n",
    "    # calculate Force and add it to the list, F\n",
    "    for dist in r:\n",
    "        force = G*(m1*m2)/(dist**2)\n",
    "        F.append(force)\n",
    "    # call the draw_graph function\n",
    "    draw_graph(r, F)\n",
    "\n",
    "if __name__=='__main__':\n",
    "    generate_F_r()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the initial velocity (m/s): sds\n",
      "You entered an invalid input\n"
     ]
    }
   ],
   "source": [
    "#P50: Draw the trajectory of a projectile motion\n",
    "\n",
    "'''\n",
    "Draw the trajectory of a body in projectile motion\n",
    "'''\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "def draw_graph(x, y):\n",
    "    plt.plot(x, y)\n",
    "    plt.xlabel('x-coordinate')\n",
    "    plt.ylabel('y-coordinate')\n",
    "    plt.title('Projectile motion of a ball')\n",
    "    \n",
    "def frange(start, final, interval):\n",
    "    numbers = []\n",
    "    while start < final:\n",
    "        numbers.append(start)\n",
    "        start = start + interval\n",
    "    return numbers\n",
    "\n",
    "def draw_trajectory(u, theta):\n",
    "    theta = math.radians(theta)\n",
    "    g = 9.8\n",
    "    # Time of flight\n",
    "    t_flight = 2*u*math.sin(theta)/g\n",
    "    # find time intervals\n",
    "    intervals = frange(0, t_flight, 0.001)\n",
    "    # list of x and y coordinates\n",
    "    x = []\n",
    "    y = []\n",
    "    for t in intervals:\n",
    "        x.append(u*math.cos(theta)*t)\n",
    "        y.append(u*math.sin(theta)*t - 0.5*g*t*t)\n",
    "    draw_graph(x, y)\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    try:\n",
    "        u = float(input('Enter the initial velocity (m/s): '))\n",
    "        theta = float(input('Enter the angle of projection (degrees): '))\n",
    "    except ValueError:\n",
    "        print('You entered an invalid input')\n",
    "    else:        \n",
    "        draw_trajectory(u, theta)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KFPXry0RmPXuajkQp7xU+E/OePZArl+loLMkdieJToDSPjszeC7yX0IMmguck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\nSimllFJKKaWUUkoppZRSKrH+DxF95xUrH6rkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e1af34f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P53: Comparing the trajectory at different intial velocities\n",
    "if __name__ == '__main__':\n",
    "    # list of three different initial velocity\n",
    "    u_list = [20, 40, 60]\n",
    "    theta = 45\n",
    "    for u in u_list:\n",
    "        draw_trajectory(u, theta)\n",
    "    # Add a legend and show the graph\n",
    "    plt.legend(['20', '40', '60'])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=-1 y=0\n",
      "x=1 y=4\n",
      "x=2 y=9\n",
      "x=3 y=16\n",
      "x=4 y=25\n",
      "x=5 y=36\n"
     ]
    }
   ],
   "source": [
    "#P55: Quadratic function calculator\n",
    "'''\n",
    "Quadratic function calculator\n",
    "'''\n",
    "# assume values of x\n",
    "x_values = [-1, 1, 2, 3, 4, 5]\n",
    "for x in x_values:\n",
    "    # calculate the value of the quadratic\n",
    "    # function\n",
    "    y = x**2 + 2*x + 1\n",
    "    print('x={0} y={1}'.format(x, y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068034a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## P58: Horizontal bar chart example\n",
    "\n",
    "'''\n",
    "Example of drawing a horizontal bar chart\n",
    "'''\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "def create_bar_chart(data, labels):\n",
    "    # number of bars\n",
    "    num_bars = len(data)\n",
    "    # this list is the point on the y-axis where each\n",
    "    # bar is centered. Here it will be [1, 2, 3..]\n",
    "    positions = range(1, num_bars+1)\n",
    "    plt.barh(positions, data, align='center')\n",
    "    # set the label of each bar\n",
    "    plt.yticks(positions, labels)\n",
    "    plt.xlabel('Steps')\n",
    "    plt.ylabel('Day')\n",
    "    plt.title('Number of steps walked')\n",
    "    # Turns on the grid which may assist in visual estimation\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Number of steps I walked during the past week\n",
    "    steps = [6534, 7000, 8900, 10786, 3467, 11045, 5095]\n",
    "    # Corresponding days\n",
    "    labels = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n",
    "    create_bar_chart(steps, labels)\n",
    "    \n",
    "   \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the initial velocity (m/s): 60\n",
      "Enter the angle of projection (degrees): 25\n"
     ]
    }
   ],
   "source": [
    "##P50: Draw the trajectory of a projectile motion using list comprehensions (Appendix B)\n",
    "\n",
    "'''\n",
    "Draw the trajectory of a body in projectile motion\n",
    "'''\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "def draw_graph(x, y):\n",
    "    plt.plot(x, y)\n",
    "    plt.xlabel('x-coordinate')\n",
    "    plt.ylabel('y-coordinate')\n",
    "    plt.title('Projectile motion of a ball')\n",
    "    \n",
    "def frange(start, final, interval):\n",
    "    numbers = []\n",
    "    while start < final:\n",
    "        numbers.append(start)\n",
    "        start = start + interval\n",
    "    return numbers\n",
    "\n",
    "def draw_trajectory(u, theta):\n",
    "    theta = math.radians(theta)\n",
    "    g = 9.8\n",
    "    # Time of flight\n",
    "    t_flight = 2*u*math.sin(theta)/g\n",
    "    # find time intervals\n",
    "    intervals = frange(0, t_flight, 0.001)\n",
    "    # list of x and y coordinates\n",
    "    x = [ u*math.cos(theta)*t for t in intervals]\n",
    "    y = [u*math.sin(theta)*t - 0.5*g*t*t for t in intervals]\n",
    "    draw_graph(x, y)\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    try:\n",
    "        u = float(input('Enter the initial velocity (m/s): '))\n",
    "        theta = float(input('Enter the angle of projection (degrees): '))\n",
    "    except ValueError:\n",
    "        print('You entered an invalid input')\n",
    "    else:        \n",
    "        draw_trajectory(u, theta)\n",
    "        plt.show()\n"
   ]
  }
 ],
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